About: Discovery of diagnostic and prognostic molecular markers is important and actively pursued the research field in cancer research. For complex diseases, this process is often performed using Machine Learning. The current study compares two approaches for the discovery of relevant variables: by application of a single feature selection algorithm, versus by an ensemble of diverse algorithms. These approaches are used to identify variables that are relevant discerning of four cancer types using RNA-seq profiles from the Cancer Genome Atlas. The comparison is carried out in two directions: evaluating the predictive performance of models and monitoring the stability of selected variables. The most informative features are identified using a four feature selection algorithms, namely U-test, ReliefF, and two variants of the MDFS algorithm. Discerning normal and tumor tissues is performed using the Random Forest algorithm. The highest stability of the feature set was obtained when U-test was used. Unfortunately, models built on feature sets obtained from the ensemble of feature selection algorithms were no better than for models developed on feature sets obtained from individual algorithms. On the other hand, the feature selectors leading to the best classification results varied between data sets.

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About: Discovery of diagnostic and prognostic molecular markers is important and actively pursued the research field in cancer research. For complex diseases, this process is often performed using Machine Learning. The current study compares two approaches for the discovery of relevant variables: by application of a single feature selection algorithm, versus by an ensemble of diverse algorithms. These approaches are used to identify variables that are relevant discerning of four cancer types using RNA-seq profiles from the Cancer Genome Atlas. The comparison is carried out in two directions: evaluating the predictive performance of models and monitoring the stability of selected variables. The most informative features are identified using a four feature selection algorithms, namely U-test, ReliefF, and two variants of the MDFS algorithm. Discerning normal and tumor tissues is performed using the Random Forest algorithm. The highest stability of the feature set was obtained when U-test was used. Unfortunately, models built on feature sets obtained from the ensemble of feature selection algorithms were no better than for models developed on feature sets obtained from individual algorithms. On the other hand, the feature selectors leading to the best classification results varied between data sets. Goto Sponge NotDistinct Permalink

An Entity of Type : fabio:Abstract, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	abstract
value	Discovery of diagnostic and prognostic molecular markers is important and actively pursued the research field in cancer research. For complex diseases, this process is often performed using Machine Learning. The current study compares two approaches for the discovery of relevant variables: by application of a single feature selection algorithm, versus by an ensemble of diverse algorithms. These approaches are used to identify variables that are relevant discerning of four cancer types using RNA-seq profiles from the Cancer Genome Atlas. The comparison is carried out in two directions: evaluating the predictive performance of models and monitoring the stability of selected variables. The most informative features are identified using a four feature selection algorithms, namely U-test, ReliefF, and two variants of the MDFS algorithm. Discerning normal and tumor tissues is performed using the Random Forest algorithm. The highest stability of the feature set was obtained when U-test was used. Unfortunately, models built on feature sets obtained from the ensemble of feature selection algorithms were no better than for models developed on feature sets obtained from individual algorithms. On the other hand, the feature selectors leading to the best classification results varied between data sets.
subject	Algorithms Data mining Model selection Machine learning Chemical compounds »more»
part of	Analysis of Ensemble Feature Selection for Correlated High-Dimensional RNA-Seq Cancer Data
is abstract of	Analysis of Ensemble Feature Selection for Correlated High-Dimensional RNA-Seq Cancer Data
is hasSource of	covid:ann/target/450d67d5594c353ce879012c5fdaf45d8c736494 covid:ann/target/7838a5607ebdb0634b1775519e809a828a2441d8 covid:ann/target/38229f95da7abccafe422817f5c900b9f23c4018 covid:ann/target/ed25cfb800518ff3f2ced23c03cc3a251c746673 covid:ann/target/7a1e1b236360635e48bb28413f40dd77a0173ced »more»

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